How to stop fearing AI and increase profit with technology

This fear was born out of past experience with IT rollouts.

How many such stories have we (and you) heard: at first the company expected to invest two million and launch an MVP in three months.

But a year and a half goes by, the invoices already exceed 15 million — and the system still isn't live.

Endless development is such a common problem that an IT project we launch on schedule often surprises clients. With AI there is also a risk of getting stuck in an endless rollout with a ballooning budget.

Add to that the unpredictable payoff from integration, and it becomes clear why businesses don't want to take the risk.

Start your AI rollout by simplifying small processes and routine tasks. For example, an AI assistant for handling call information can be integrated in a month, and it will take on tasks such as:

It makes it easier to find agreements through a chatbot.

You and your staff no longer have to reread hundreds of emails and chats to find the information you need.

Just ask the AI assistant a question and get an answer in 15 seconds.

It analyzes the call against important parameters: setting the agenda, adherence to the script, the manager's politeness, willingness to solve the client's problems, recording agreements, rapport markers, and so on.

It automatically emails the summary to you and the client, and updates the client record in the CRM and the project documentation.

To do this, the bot transcribes the call and highlights the key points. The summary shows the main ideas of the conversation — no need to spend time rewatching the recording or reading the full transcript.

Pick one area where you work with a large amount of documentation and audio information.

As a rule, it eats up a lot of staff time spent saving and searching for the data they need. 2.

Together with the developer, define which integrations the AI assistant needs to work effectively: conferencing, data storage, CRM, messengers, and so on. As a result, the cost of such an AI assistant can be forecast — it is made up of two parts.

Rollout cost. What needs to be done to get the system working at a specific company.

It depends on the number of meetings to process and the chosen language model. For example, KT.Team uses the TL;DV transcriber under the hood and pays 4,000 rubles a month for it (about 3,000 calls). Of course, if your needs are bigger, the subscription will cost more — but the amount will still be incomparably lower than the benefit to the business.

Start with solutions for minor processes: they are quick and cheap to roll out. You'll see how AI eases the work and decide which more complex process to automate next.

In business, processes are constantly changing. For example, today a company works with construction tenders and configures its AI processes to handle these tasks.

Tomorrow the entrepreneur decides to also take part in design tenders. It seems that all the algorithms will have to be rewritten from scratch and checked to make sure everything works correctly. So does that mean investing another couple of million in a new AI assistant? No, there is no need to implement a new AI assistant.

The target orders and the method of assessing the technical specification have changed — but most of the automated stages remain the same. For example, tenders still need to be collected from the same platforms, and they still need to be analyzed for relevance and the chance of winning (whether there are signs of an inside-deal tender).

Regardless of the technical specification, you need to collect information from the required documents.

There is no need to retrain the assistant for these stages.

But some refinement and additional training will still be needed: you will have to add new keywords to select tenders of interest.

A new method for assessing the technical specification will need to be developed — after all, the list of tasks in a design tender differs from a construction order.

But the additional training process is many times faster and cheaper than implementing a new assistant: you simply load the up-to-date guidelines and files into the AI system.

Then these guidelines are automatically loaded into your assistant, after which you run tests — and it's all set.

At our company, AI is used by HR specialists, project managers and developers.

Here is what we saw after KT.Team employees started using AI assistants. HR.

We automated pulse surveys: the HR assistant processes survey data and highlights pain points, growth areas and work problems for each employee. HR specialists focused on addressing these pain points — as a result, we were able to raise the loyalty index (eNPS).

Project manager. The AI assistant transcribes all calls word for word and stores them.

When a misunderstanding arises on a project or previous agreements need to be revisited, the project manager can ask the AI assistant about them within a few minutes and get a detailed answer with a reference to the source, date and time.

The manager can focus on communicating with the team and clients instead of spending hours re-listening to old meetings. Developer.

The key thing in a developer's work is to understand the task correctly: what value the feature should bring and what result the client wants to achieve.

Using AI as a copilot helps with this. AI frees up a specialist's time from the mechanical writing of code and gives more room to clarify the client's requirements, goals and wishes; to think through the feature's logic and to define constraints.

Every company may have its own fears that keep it from bringing AI into its workflows. For example, IT specialists often worry that confidential data will leak when working with neural networks.

Every problem can be solved — to avoid handling the rollout yourself, get in touch with us.

We'll discuss your tasks and propose a solution that works specifically for your business.

We'll deploy the system in the cloud, on your servers, or propose a hybrid option, account for security and workflow policies, and stay in touch throughout the entire rollout. So which tasks would you like to hand over to AI?